Recap of Data Cleaning

  • is.na(),any(is.na()), count(), and functions from naniar like gg_miss_var() can help determine if we have NA values
  • filter() automatically removes NA values - can’t confirm or deny if condition is met (need | is.na() to keep them)
  • drop_na() can help you remove NA values from a variable or an entire data frame
  • NA values can change your calculation results
  • think about what NA values represent

Recap of Data Cleaning

  • recode() can help with simple recoding (not based on condition but simple swap)
  • case_when() can recode entire values based on conditions
    • remember case_when() needs TRUE ~ varaible to keep values that aren’t specified by conditions, otherwise will be NA
  • stringr package has great functions for looking for specific parts of values especially filter() and str_detect() combined
    • also has other useful string manipulation functions like str_replace() and more!
    • separate() can split columns into additional columns
    • unite() can combine columns

📃Cheatsheet

Manipulating Data

In this module, we will show you how to:

  1. Reshape data from wide to long
  2. Reshape data from long to wide
  3. Merge Data/Joins

What is wide/long data?

Data is wide or long with respect to certain variables.

Wide versus long data rearanges the position of column names and row content.

What is wide/long data?

Data is stored differently in the tibble.

Wide: has many columns

# A tibble: 1 × 4
  State   June_vacc_rate May_vacc_rate April_vacc_rate
  <chr>            <dbl>         <dbl>           <dbl>
1 Alabama          0.516         0.514           0.511

Long: column names become data

# A tibble: 3 × 3
  State   name            value
  <chr>   <chr>           <dbl>
1 Alabama June_vacc_rate  0.516
2 Alabama May_vacc_rate   0.514
3 Alabama April_vacc_rate 0.511

What is wide/long data?

Wide: multiple columns per individual, values spread across multiple columns

# A tibble: 2 × 4
  State   June_vacc_rate May_vacc_rate April_vacc_rate
  <chr>            <dbl>         <dbl>           <dbl>
1 Alabama          0.516         0.514           0.511
2 Alaska           0.627         0.626           0.623

Long: multiple rows per observation, a single column contains the values

# A tibble: 6 × 3
  State   name            value
  <chr>   <chr>           <dbl>
1 Alabama June_vacc_rate  0.516
2 Alabama May_vacc_rate   0.514
3 Alabama April_vacc_rate 0.511
4 Alaska  June_vacc_rate  0.627
5 Alaska  May_vacc_rate   0.626
6 Alaska  April_vacc_rate 0.623

What is wide/long data?

Why do we need to switch between wide/long data?

Wide: Easier for humans to read

# A tibble: 2 × 4
  State   June_vacc_rate May_vacc_rate April_vacc_rate
  <chr>            <dbl>         <dbl>           <dbl>
1 Alabama          0.516         0.514           0.511
2 Alaska           0.627         0.626           0.623

Long: Easier for R to make plots & do analysis

# A tibble: 6 × 3
  State   name            value
  <chr>   <chr>           <dbl>
1 Alabama June_vacc_rate  0.516
2 Alabama May_vacc_rate   0.514
3 Alabama April_vacc_rate 0.511
4 Alaska  June_vacc_rate  0.627
5 Alaska  May_vacc_rate   0.626
6 Alaska  April_vacc_rate 0.623

Pivoting using tidyr package

tidyr allows you to “tidy” your data. We will be talking about:

  • pivot_longer - make multiple columns into variables, (wide to long)
  • pivot_wider - make a variable into multiple columns, (long to wide)
  • separate - string into multiple columns (review)

The reshape command exists. Its arguments are considered more confusing, so we don’t recommend it.

You might see old functions gather and spread when googling. These are older iterations of pivot_longer and pivot_wider, respectively.

pivot_longer…

Reshaping data from wide to long

pivot_longer() - puts column data into rows (tidyr package)

  • First describe which columns we want to “pivot_longer”
{long_data} <- {wide_data} %>% pivot_longer(cols = {columns to pivot})

Reshaping data from wide to long

wide_data
# A tibble: 1 × 3
  June_vacc_rate May_vacc_rate April_vacc_rate
           <dbl>         <dbl>           <dbl>
1          0.516         0.514           0.511
long_data <- wide_data %>% pivot_longer(cols = everything())
long_data
# A tibble: 3 × 2
  name            value
  <chr>           <dbl>
1 June_vacc_rate  0.516
2 May_vacc_rate   0.514
3 April_vacc_rate 0.511

Reshaping data from wide to long

pivot_longer() - puts column data into rows (tidyr package)

  • First describe which columns we want to “pivot_longer”
  • names_to = gives a new name to the pivoted columns
  • values_to = gives a new name to the values that used to be in those columns
{long_data} <- {wide_data} %>% pivot_longer(cols = {columns to pivot},
                                        names_to = {New column name: contains old column names},
                                        values_to = {New column name: contains cell values})

Reshaping data from wide to long

wide_data
# A tibble: 1 × 3
  June_vacc_rate May_vacc_rate April_vacc_rate
           <dbl>         <dbl>           <dbl>
1          0.516         0.514           0.511
long_data <- wide_data %>% pivot_longer(cols = everything(),
                                        names_to = "Month",
                                        values_to = "Rate")
long_data
# A tibble: 3 × 2
  Month            Rate
  <chr>           <dbl>
1 June_vacc_rate  0.516
2 May_vacc_rate   0.514
3 April_vacc_rate 0.511

Newly created column names are enclosed in quotation marks.

Data used: Charm City Circulator

http://jhudatascience.org/intro_to_r/data/Charm_City_Circulator_Ridership.csv

library(jhur)
circ <- read_circulator()
head(circ, 5)
# A tibble: 5 × 15
  day       date  orangeBoardings orangeAlightings orangeAverage purpleBoardings
  <chr>     <chr>           <dbl>            <dbl>         <dbl>           <dbl>
1 Monday    01/1…             877             1027          952               NA
2 Tuesday   01/1…             777              815          796               NA
3 Wednesday 01/1…            1203             1220         1212.              NA
4 Thursday  01/1…            1194             1233         1214.              NA
5 Friday    01/1…            1645             1643         1644               NA
# ℹ 9 more variables: purpleAlightings <dbl>, purpleAverage <dbl>,
#   greenBoardings <dbl>, greenAlightings <dbl>, greenAverage <dbl>,
#   bannerBoardings <dbl>, bannerAlightings <dbl>, bannerAverage <dbl>,
#   daily <dbl>

Mission: Taking the averages by line

Let’s imagine we want to create a table of average ridership by route/line. Results should look something like:

example <- tibble(line = c("orange","purple","green","banner"),
                  avg = c("600(?)", "700(?)", "500(?)", "400(?)")
)
example
# A tibble: 4 × 2
  line   avg   
  <chr>  <chr> 
1 orange 600(?)
2 purple 700(?)
3 green  500(?)
4 banner 400(?)

Reshaping data from wide to long

long <- circ %>% 
  pivot_longer(starts_with(c("orange","purple","green","banner")))
long
# A tibble: 13,752 × 5
   day    date       daily name             value
   <chr>  <chr>      <dbl> <chr>            <dbl>
 1 Monday 01/11/2010   952 orangeBoardings    877
 2 Monday 01/11/2010   952 orangeAlightings  1027
 3 Monday 01/11/2010   952 orangeAverage      952
 4 Monday 01/11/2010   952 purpleBoardings     NA
 5 Monday 01/11/2010   952 purpleAlightings    NA
 6 Monday 01/11/2010   952 purpleAverage       NA
 7 Monday 01/11/2010   952 greenBoardings      NA
 8 Monday 01/11/2010   952 greenAlightings     NA
 9 Monday 01/11/2010   952 greenAverage        NA
10 Monday 01/11/2010   952 bannerBoardings     NA
# ℹ 13,742 more rows

Reshaping data from wide to long

Un-pivoted columns appear the same as before (day, date, daily)

head(circ, n = 2)
# A tibble: 2 × 15
  day     date    orangeBoardings orangeAlightings orangeAverage purpleBoardings
  <chr>   <chr>             <dbl>            <dbl>         <dbl>           <dbl>
1 Monday  01/11/…             877             1027           952              NA
2 Tuesday 01/12/…             777              815           796              NA
# ℹ 9 more variables: purpleAlightings <dbl>, purpleAverage <dbl>,
#   greenBoardings <dbl>, greenAlightings <dbl>, greenAverage <dbl>,
#   bannerBoardings <dbl>, bannerAlightings <dbl>, bannerAverage <dbl>,
#   daily <dbl>
head(long, n = 2)
# A tibble: 2 × 5
  day    date       daily name             value
  <chr>  <chr>      <dbl> <chr>            <dbl>
1 Monday 01/11/2010   952 orangeBoardings    877
2 Monday 01/11/2010   952 orangeAlightings  1027

Cleaning up long data

We will use str_replace from the stringr package to put _ in the names

long <- long %>% mutate(
  name = str_replace(name, "Board", "_Board"),
  name = str_replace(name, "Alight", "_Alight"),
  name = str_replace(name, "Average", "_Average") 
)
long
# A tibble: 13,752 × 5
   day    date       daily name              value
   <chr>  <chr>      <dbl> <chr>             <dbl>
 1 Monday 01/11/2010   952 orange_Boardings    877
 2 Monday 01/11/2010   952 orange_Alightings  1027
 3 Monday 01/11/2010   952 orange_Average      952
 4 Monday 01/11/2010   952 purple_Boardings     NA
 5 Monday 01/11/2010   952 purple_Alightings    NA
 6 Monday 01/11/2010   952 purple_Average       NA
 7 Monday 01/11/2010   952 green_Boardings      NA
 8 Monday 01/11/2010   952 green_Alightings     NA
 9 Monday 01/11/2010   952 green_Average        NA
10 Monday 01/11/2010   952 banner_Boardings     NA
# ℹ 13,742 more rows

Cleaning up long data with separate()

  • first argument - which column should be split up?
  • “into =” gives names to the new columns
  • “sep =” to show where the separation should happen.
long <- long %>% 
  separate(name, into = c("line", "type"), sep = "_")
long
# A tibble: 13,752 × 6
   day    date       daily line   type       value
   <chr>  <chr>      <dbl> <chr>  <chr>      <dbl>
 1 Monday 01/11/2010   952 orange Boardings    877
 2 Monday 01/11/2010   952 orange Alightings  1027
 3 Monday 01/11/2010   952 orange Average      952
 4 Monday 01/11/2010   952 purple Boardings     NA
 5 Monday 01/11/2010   952 purple Alightings    NA
 6 Monday 01/11/2010   952 purple Average       NA
 7 Monday 01/11/2010   952 green  Boardings     NA
 8 Monday 01/11/2010   952 green  Alightings    NA
 9 Monday 01/11/2010   952 green  Average       NA
10 Monday 01/11/2010   952 banner Boardings     NA
# ℹ 13,742 more rows

Mission: Taking the averages by line

Now our data is more tidy, and we can take the averages easily!

long %>% 
  group_by(line) %>% 
  summarize("avg" = mean(value, na.rm = TRUE))
# A tibble: 4 × 2
  line     avg
  <chr>  <dbl>
1 banner  827.
2 green  1958.
3 orange 3056.
4 purple 4066.

Reshaping data from wide to long

There are many ways to select the columns we want. Check out https://dplyr.tidyverse.org/reference/dplyr_tidy_select.html to look at more column selection options.

circ %>% 
  pivot_longer( !c(day, date, daily))
# A tibble: 13,752 × 5
   day    date       daily name             value
   <chr>  <chr>      <dbl> <chr>            <dbl>
 1 Monday 01/11/2010   952 orangeBoardings    877
 2 Monday 01/11/2010   952 orangeAlightings  1027
 3 Monday 01/11/2010   952 orangeAverage      952
 4 Monday 01/11/2010   952 purpleBoardings     NA
 5 Monday 01/11/2010   952 purpleAlightings    NA
 6 Monday 01/11/2010   952 purpleAverage       NA
 7 Monday 01/11/2010   952 greenBoardings      NA
 8 Monday 01/11/2010   952 greenAlightings     NA
 9 Monday 01/11/2010   952 greenAverage        NA
10 Monday 01/11/2010   952 bannerBoardings     NA
# ℹ 13,742 more rows

pivot_wider…

Reshaping data from long to wide

pivot_wider() - spreads row data into columns (tidyr package)

  • names_from = the old column whose contents will be spread into multiple new column names.
  • values_from = the old column whose contents will fill in the values of those new columns.
{wide_data} <- {long_data} %>% 
  pivot_wider(names_from = {Old column name: contains new column names},
              values_from = {Old column name: contains new cell values})

Reshaping data from long to wide

long_data
# A tibble: 3 × 2
  Month            Rate
  <chr>           <dbl>
1 June_vacc_rate  0.516
2 May_vacc_rate   0.514
3 April_vacc_rate 0.511
wide_data <- long_data %>% pivot_wider(names_from = "Month", 
                                       values_from = "Rate") 
wide_data
# A tibble: 1 × 3
  June_vacc_rate May_vacc_rate April_vacc_rate
           <dbl>         <dbl>           <dbl>
1          0.516         0.514           0.511

Reshaping Charm City Circulator

long
# A tibble: 13,752 × 6
   day    date       daily line   type       value
   <chr>  <chr>      <dbl> <chr>  <chr>      <dbl>
 1 Monday 01/11/2010   952 orange Boardings    877
 2 Monday 01/11/2010   952 orange Alightings  1027
 3 Monday 01/11/2010   952 orange Average      952
 4 Monday 01/11/2010   952 purple Boardings     NA
 5 Monday 01/11/2010   952 purple Alightings    NA
 6 Monday 01/11/2010   952 purple Average       NA
 7 Monday 01/11/2010   952 green  Boardings     NA
 8 Monday 01/11/2010   952 green  Alightings    NA
 9 Monday 01/11/2010   952 green  Average       NA
10 Monday 01/11/2010   952 banner Boardings     NA
# ℹ 13,742 more rows

Reshaping Charm City Circulator

wide <- long %>% pivot_wider(names_from = "type", 
                             values_from = "value") 
wide
# A tibble: 4,584 × 7
   day       date       daily line   Boardings Alightings Average
   <chr>     <chr>      <dbl> <chr>      <dbl>      <dbl>   <dbl>
 1 Monday    01/11/2010  952  orange       877       1027    952 
 2 Monday    01/11/2010  952  purple        NA         NA     NA 
 3 Monday    01/11/2010  952  green         NA         NA     NA 
 4 Monday    01/11/2010  952  banner        NA         NA     NA 
 5 Tuesday   01/12/2010  796  orange       777        815    796 
 6 Tuesday   01/12/2010  796  purple        NA         NA     NA 
 7 Tuesday   01/12/2010  796  green         NA         NA     NA 
 8 Tuesday   01/12/2010  796  banner        NA         NA     NA 
 9 Wednesday 01/13/2010 1212. orange      1203       1220   1212.
10 Wednesday 01/13/2010 1212. purple        NA         NA     NA 
# ℹ 4,574 more rows

Summary

  • tidyr package helps us convert between wide and long data
  • pivot_longer() goes from wide -> long
    • Specify columns you want to pivot
    • Specify names_to = and values_to = for custom naming
  • pivot_wider() goes from long -> wide
    • Specify names_from = and values_from =

Lab Part 1

Joining

“Combining datasets”

Inner, outer, left, and right joins represented with venn diagrams

Joining in dplyr

  • Merging/joining data sets together - usually on key variables, usually “id”
  • ?join - see different types of joining for dplyr
  • inner_join(x, y) - only rows that match for x and y are kept
  • full_join(x, y) - all rows of x and y are kept
  • left_join(x, y) - all rows of x are kept even if not merged with y
  • right_join(x, y) - all rows of y are kept even if not merged with x
  • anti_join(x, y) - all rows from x not in y keeping just columns from x.

Merging: Simple Data

data_As
# A tibble: 2 × 3
  State   June_vacc_rate May_vacc_rate
  <chr>            <dbl>         <dbl>
1 Alabama          0.516         0.514
2 Alaska           0.627         0.626
data_cold
# A tibble: 2 × 2
  State  April_vacc_rate
  <chr>            <dbl>
1 Maine            0.795
2 Alaska           0.623

Inner Join

Inner Join

ij <- inner_join(data_As, data_cold)
Joining with `by = join_by(State)`
ij
# A tibble: 1 × 4
  State  June_vacc_rate May_vacc_rate April_vacc_rate
  <chr>           <dbl>         <dbl>           <dbl>
1 Alaska          0.627         0.626           0.623

Left Join

Left Join

lj <- left_join(data_As, data_cold)
Joining with `by = join_by(State)`
lj
# A tibble: 2 × 4
  State   June_vacc_rate May_vacc_rate April_vacc_rate
  <chr>            <dbl>         <dbl>           <dbl>
1 Alabama          0.516         0.514          NA    
2 Alaska           0.627         0.626           0.623

Install tidylog package to log outputs

# install.packages("tidylog")
library(tidylog)
left_join(data_As, data_cold)
Joining with `by = join_by(State)`
left_join: added one column (April_vacc_rate)
> rows only in x 1
> rows only in y (1)
> matched rows 1
> ===
> rows total 2
# A tibble: 2 × 4
  State   June_vacc_rate May_vacc_rate April_vacc_rate
  <chr>            <dbl>         <dbl>           <dbl>
1 Alabama          0.516         0.514          NA    
2 Alaska           0.627         0.626           0.623

Right Join

Right Join

rj <- right_join(data_As, data_cold)
Joining with `by = join_by(State)`
right_join: added one column (April_vacc_rate)
> rows only in x (1)
> rows only in y 1
> matched rows 1
> ===
> rows total 2
rj
# A tibble: 2 × 4
  State  June_vacc_rate May_vacc_rate April_vacc_rate
  <chr>           <dbl>         <dbl>           <dbl>
1 Alaska          0.627         0.626           0.623
2 Maine          NA            NA               0.795

Left Join: Switching arguments

lj2 <- left_join(data_cold, data_As)
Joining with `by = join_by(State)`
left_join: added 2 columns (June_vacc_rate, May_vacc_rate)
> rows only in x 1
> rows only in y (1)
> matched rows 1
> ===
> rows total 2
lj2
# A tibble: 2 × 4
  State  April_vacc_rate June_vacc_rate May_vacc_rate
  <chr>            <dbl>          <dbl>         <dbl>
1 Maine            0.795         NA            NA    
2 Alaska           0.623          0.627         0.626

Full Join

Full Join

fj <- full_join(data_As, data_cold)
Joining with `by = join_by(State)`
full_join: added one column (April_vacc_rate)
> rows only in x 1
> rows only in y 1
> matched rows 1
> ===
> rows total 3
fj
# A tibble: 3 × 4
  State   June_vacc_rate May_vacc_rate April_vacc_rate
  <chr>            <dbl>         <dbl>           <dbl>
1 Alabama          0.516         0.514          NA    
2 Alaska           0.627         0.626           0.623
3 Maine           NA            NA               0.795

Watch out for “includes duplicates”

data_As
# A tibble: 2 × 2
  State   state_bird      
  <chr>   <chr>           
1 Alabama wild turkey     
2 Alaska  willow ptarmigan
data_cold
# A tibble: 3 × 3
  State  vacc_rate month
  <chr>      <dbl> <chr>
1 Maine      0.795 April
2 Alaska     0.623 April
3 Alaska     0.626 May

Watch out for “includes duplicates”

lj <- left_join(data_As, data_cold)
Joining with `by = join_by(State)`
left_join: added 2 columns (vacc_rate, month)
> rows only in x 1
> rows only in y (1)
> matched rows 2 (includes duplicates)
> ===
> rows total 3

Watch out for “includes duplicates”

Data including the joining column (“State”) has been duplicated.

lj
# A tibble: 3 × 4
  State   state_bird       vacc_rate month
  <chr>   <chr>                <dbl> <chr>
1 Alabama wild turkey         NA     <NA> 
2 Alaska  willow ptarmigan     0.623 April
3 Alaska  willow ptarmigan     0.626 May


Note that “Alaska willow ptarmigan” appears twice.

Watch out for “includes duplicates”

Stop tidylog

unloadNamespace("tidylog")

Using the by argument

By default joins use the intersection of column names. If by is specified, it uses that.

full_join(data_As, data_cold, by = "State")
# A tibble: 4 × 4
  State   state_bird       vacc_rate month
  <chr>   <chr>                <dbl> <chr>
1 Alabama wild turkey         NA     <NA> 
2 Alaska  willow ptarmigan     0.623 April
3 Alaska  willow ptarmigan     0.626 May  
4 Maine   <NA>                 0.795 April

Using the by argument

You can join based on multiple columns by using something like by = c(col1, col2).

If the datasets have two different names for the same data, use:

full_join(x, y, by = c("a" = "b"))

Using “setdiff”

We might want to determine what indexes ARE in the first dataset that AREN’T in the second:

data_As
# A tibble: 2 × 2
  State   state_bird      
  <chr>   <chr>           
1 Alabama wild turkey     
2 Alaska  willow ptarmigan
data_cold
# A tibble: 3 × 3
  State  vacc_rate month
  <chr>      <dbl> <chr>
1 Maine      0.795 April
2 Alaska     0.623 April
3 Alaska     0.626 May

Using “setdiff”

Use setdiff to determine what indexes ARE in the first dataset that AREN’T in the second:

A_states <- data_As %>% pull(State)
cold_states <- data_cold %>% pull(State)
setdiff(A_states, cold_states)
[1] "Alabama"
setdiff(cold_states, A_states)
[1] "Maine"

Summary

  • Merging/joining data sets together - assumes all column names that overlap
    • use the by = c("a" = "b") if they differ
  • inner_join(x, y) - only rows that match for x and y are kept
  • full_join(x, y) - all rows of x and y are kept
  • left_join(x, y) - all rows of x are kept even if not merged with y
  • right_join(x, y) - all rows of y are kept even if not merged with x
  • Use the tidylog package for a detailed summary
  • setdiff(x, y) shows what in x is missing from y

Lab Part 2

Additional Slides

Inconsistencies in non-pivoted columns?

Notice “daily” column has different values

long2
# A tibble: 13,752 × 6
   day    date       daily line   type       value
   <chr>  <chr>      <dbl> <chr>  <chr>      <dbl>
 1 Monday 01/11/2010   952 orange Boardings    877
 2 MONDAY 01/11/2010   952 orange Alightings  1027
 3 Monday 01/11/2010   952 orange Average      952
 4 Monday 01/11/2010   952 purple Boardings     NA
 5 Monday 01/11/2010   952 purple Alightings    NA
 6 Monday 01/11/2010   952 purple Average       NA
 7 Monday 01/11/2010   952 green  Boardings     NA
 8 Monday 01/11/2010   952 green  Alightings    NA
 9 Monday 01/11/2010   952 green  Average       NA
10 Monday 01/11/2010   952 banner Boardings     NA
# ℹ 13,742 more rows

Inconsistencies in non-pivoted columns?

R won’t drop data while pivoting.

wide2 <- long2 %>% pivot_wider(names_from = "type", 
                             values_from = "value") 
wide2
# A tibble: 4,585 × 7
   day       date       daily line   Boardings Alightings Average
   <chr>     <chr>      <dbl> <chr>      <dbl>      <dbl>   <dbl>
 1 Monday    01/11/2010  952  orange       877         NA    952 
 2 MONDAY    01/11/2010  952  orange        NA       1027     NA 
 3 Monday    01/11/2010  952  purple        NA         NA     NA 
 4 Monday    01/11/2010  952  green         NA         NA     NA 
 5 Monday    01/11/2010  952  banner        NA         NA     NA 
 6 Tuesday   01/12/2010  796  orange       777        815    796 
 7 Tuesday   01/12/2010  796  purple        NA         NA     NA 
 8 Tuesday   01/12/2010  796  green         NA         NA     NA 
 9 Tuesday   01/12/2010  796  banner        NA         NA     NA 
10 Wednesday 01/13/2010 1212. orange      1203       1220   1212.
# ℹ 4,575 more rows